Loop closing and relocalization are crucial techniques to establish reliable
and robust long-term SLAM by addressing pose estimation drift and degeneration.
This article begins by formulating loop closing and relocalization within a
unified framework. Then, we propose a novel multi-head network LCR-Net to
tackle both tasks effectively. It exploits novel feature extraction and
pose-aware attention mechanism to precisely estimate similarities and 6-DoF
poses between pairs of LiDAR scans. In the end, we integrate our LCR-Net into a
SLAM system and achieve robust and accurate online LiDAR SLAM in outdoor
driving environments. We thoroughly evaluate our LCR-Net through three setups
derived from loop closing and relocalization, including candidate retrieval,
closed-loop point cloud registration, and continuous relocalization using
multiple datasets. The results demonstrate that LCR-Net excels in all three
tasks, surpassing the state-of-the-art methods and exhibiting a remarkable
generalization ability. Notably, our LCR-Net outperforms baseline methods
without using a time-consuming robust pose estimator, rendering it suitable for
online SLAM applications. To our best knowledge, the integration of LCR-Net
yields the first LiDAR SLAM with the capability of deep loop closing and
relocalization. The implementation of our methods will be made open-source.

该研究聚焦于解决姿态估计漂移和退化问题，提出了一种名为 LCR-Net 的多头网络模型，通过新颖的特征提取和姿态感知机制来准确估计 LiDAR 扫描之间的相似度和 6-DoF 姿态，将其应用于室外驾驶环境中，实现了强大而准确的在线 LiDAR SLAM，同时在候选项检索、闭环点云配准和连续重定位等三个设置中全面评估并展示了优异的性能，超过了同类方法并表现出卓越的泛化能力，它不依赖于耗时的鲁棒姿态估计器，适用于在线 SLAM 应用。这是首个具有深度闭环检测和重定位能力的 LiDAR SLAM。

快速准确的深度闭环和可靠的激光雷达定位与建图

Fast and Accurate Deep Loop Closing and Relocalization for Reliable  LiDAR SLAM

Simultaneous localization and mapping (SLAM) is a fundamental capability
required by most autonomous systems. In this paper, we address the problem of
loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our
approach utilizes a deep neural network exploiting different cues generated
from LiDAR data for finding loop closures. It estimates an image overlap
generalized to range images and provides a relative yaw angle estimate between
pairs of scans. Based on such predictions, we tackle loop closure detection and
integrate our approach into an existing SLAM system to improve its mapping
results. We evaluate our approach on sequences of the KITTI odometry benchmark
and the Ford campus dataset. We show that our method can effectively detect
loop closures surpassing the detection performance of state-of-the-art methods.
To highlight the generalization capabilities of our approach, we evaluate our
model on the Ford campus dataset while using only KITTI for training. The
experiments show that the learned representation is able to provide reliable
loop closure candidates, also in unseen environments.

本文介绍了使用深度神经网络在自动驾驶汽车记录的 3D 激光扫描数据中基于各种线索发现循环闭合的方法，并将其集成到现有的 SLAM 系统中，结果表明它可以有效地检测循环闭合，从而提高 SLAM 系统的映射效果，并通过 KITI 和 Ford 数据集进行了评估。

OverlapNet: 基于激光雷达 SLAM 的环路闭合

OverlapNet: Loop Closing for LiDAR-based SLAM

This paper presents ORB-SLAM, a feature-based monocular SLAM system that
operates in real time, in small and large, indoor and outdoor environments. The
system is robust to severe motion clutter, allows wide baseline loop closing
and relocalization, and includes full automatic initialization. Building on
excellent algorithms of recent years, we designed from scratch a novel system
that uses the same features for all SLAM tasks: tracking, mapping,
relocalization, and loop closing. A survival of the fittest strategy that
selects the points and keyframes of the reconstruction leads to excellent
robustness and generates a compact and trackable map that only grows if the
scene content changes, allowing lifelong operation. We present an exhaustive
evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves
unprecedented performance with respect to other state-of-the-art monocular SLAM
approaches. For the benefit of the community, we make the source code public.

本文介绍了 ORB-SLAM，一种基于特征的单目 SLAM 系统，能够实时运行，在小型和大型、室内和室外环境中运行，并具有鲁棒性、宽基线环路闭合和重定位等特点。